Abstract

Salience is a broad and widely used concept in neuroscience whose neuronal correlates, however, remain elusive. In behavioral conditioning, salience is used to explain various effects, such as stimulus overshadowing, and refers to how fast and strongly a stimulus can be associated with a conditioned event. Here, we identify sounds of equal intensity and perceptual detectability, which due to their spectro-temporal content recruit different levels of population activity in mouse auditory cortex. When using these sounds as cues in a Go/NoGo discrimination task, the degree of cortical recruitment matches the salience parameter of a reinforcement learning model used to analyze learning speed. We test an essential prediction of this model by training mice to discriminate light-sculpted optogenetic activity patterns in auditory cortex, and verify that cortical recruitment causally determines association or overshadowing of the stimulus components. This demonstrates that cortical recruitment underlies major aspects of stimulus salience during reinforcement learning.

Cortical recruitment differences impacts learning phase duration. a Schematics describing the auditory Go/NoGo discrimination task. b Individual learning curves for four mice discriminating sounds A and C. Performance for S+ (red), S− (light blue) and both (black) sounds are displayed. Mice from the top row have sound A as the S− stimulus while mice from the bottom row have sound C as the S− stimulus. Typical learning curves display a delay and learning phase as shown in light gray and orange colors. c Mean learning curves for different groups of mice (n = 6 for each curve) discriminating between sounds A and C (top) or A and B (bottom). Slower learning is observed when the S− sound recruits less cortical activity than the S+ sound (blue) as compared to when sound valence is swapped (orange). d Mean±standard error for the learning and delay phase for the five discriminated sound pairs (A vs C, A vs B, B vs C, up- vs down ramp, 20Hz vs 1Hz modulation represented by blue and orange symbols). The conditions “S− recruitment >S+ recruitment” (blue) and “S+ recruitment <S− recruitment” (orange) are significantly different for the learning phase but not the delay phase (Friedman test, p = 0.0005 indicated as *** and p = 0.72 indicated as ns, n = 6 mice per group except for the up- and down ramps, n = 12). e Cumulative distributions of learning phase durations for sound pairs A–C and A–B. Error bars represent standard errors (SEM)

The effects of neuronal recruitment in the model are explained by the differential adjustment of synaptic weights during learning. a (top) Sketch of the initial synaptic weights and simulated model performance for S+ (red), S− (light blue), and both stimuli (black). (middle) Values of the connections to the excitatory (G) and inhibitory decision populations as indicated by the schematics on the left-hand-side. Yellow: connections from the “common” Ĉ population. Red: connections from the Ŝ+ population. Blue: connections from the Ŝ− population. (bottom) Sketch of the connectivity pattern after and response probability after learning for the S+ (co-activation of Ŝ+& Ĉ) and S− (co-activation of Ŝ− and Ĉ) reinforced stimuli as well as for the common stimulus component alone (Ĉ, yellow). Simulation parameters: X = [1; 1; 2], α = 0.01, σ = 0.6195, v = 6, wCi = 1, wCe = 2, and wS = 0.01. b. Same as a, but with wCi = 0.1, wCe = 0.2. c Same as a, but with X = [1; 2; 1]. d Same as c, but with wCi = 0.1, wCe = 0.2